Dynamic analysis apparatus and dynamic analysis system

ABSTRACT

A dynamic analysis apparatus may include a setting section which sets a target region in a lung region of a chest dynamic image; a conversion section which calculates a representative value of a pixel signal value in the target region, and converts the pixel signal value; an extraction section which extracts a pulmonary blood flow signal from the image; and a calculation section which calculates a change in the pulmonary blood flow signal, and calculates a feature amount regarding pulmonary blood flow. The setting section may determine a size of the target region based on a size of a body part other than a lung blood vessel, a movement amount of a body part other than the lung blood vessel or subject information of the chest dynamic image, the subject information regarding a subject of the radiation imaging, and the setting section may set the target region.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a dynamic analysis apparatus and adynamic analysis system.

Description of Related Art

Attempts have been made to use semiconductor image sensors such as FPDs(flat panel detectors) for capturing dynamic images at diagnosis targetsites and use the dynamic images for diagnosis instead of capturing anddiagnosing still images of radiation (X-ray) using conventionalfilms/screens and stimulable phosphor plates. Specifically, by usingrapid responsiveness of a semiconductor image sensor in reading anddeleting image data, pulsed radiation is continuously emitted from aradiation source at the reading and deleting timings of thesemiconductor image sensor, and imaging is performed a plurality oftimes per second to capture images of a dynamic state at a diagnosistarget site. By sequentially displaying a series of images captured bythe imaging, a doctor can observe a series of movements at the diagnosistarget site.

In diagnosis of a lung, it is important to observe the lung to find apoint where a lung function (ventilation function and pulmonary bloodflow function) is lowered. However, there is difficulty in observing thedynamic state image and visually recognizing the functionally abnormalpoint for a doctor. Especially, the lung's respiratory motion and theheart beat are different between individuals, and thus, it is difficultto visually confirm the abnormal point of ventilation function andpulmonary blood flow function in consideration of the difference betweenindividuals.

Thus, it has been suggested to analyze a series of frame images obtainedby dynamic imaging, generate diagnosis support information and providethe diagnosis support information to a doctor for early diagnosis.

For example, Patent document 1 (Japanese Patent Application Laid OpenPublication No. 2014-128687) describes a system in which a lung regionin a dynamic image, which was captured while the subject was breathing,is divided into a plurality of small regions, information regarding theamount of pulmonary blood flow is generated by calculating an averagevalue of pixel values and applying a high pass filter in a timedirection for each of the small regions, and phase delay time of atemporal change of information regarding pulmonary blood flow withrespect to a temporal change of a signal indicating the heart beat iscalculated and displayed. The size of each small region is described tobe 0.4 to 4 cm square.

The movements of body parts in the lung are very large compared to thechange of pulmonary blood flow. Thus, in order to analyze the dynamicimage and obtain information regarding the change of pulmonary bloodflow function, it is preferable that the imaging is performed while thesubject holds the breath to suppress the movements of the body parts dueto breathing. However, in some cases, the breath-holding is impossibledue to diseases and such like, and an analysis method using a dynamicimage captured without holding the breath is desired.

The Patent document 1 analyzes a chest dynamic image captured under abreathing state, and suppresses the noises other than a blood flowsignal component by averaging the pixel values in the small regions andapplying a high pass filter. However, the size of small region is notappropriate for removing the influences such as the movements of bodyparts and differences between individuals. Thus, the movements of bodyparts, differences between individuals and such like appear as a noisein some cases.

SUMMARY OF THE INVENTION

An object of the present invention is to set a size of a target regionfor suppressing a noise to be an appropriate size when a pulmonary bloodflow function is analyzed in a chest dynamic image.

In order to solve the above problems, according to one aspect of thepresent invention, there is provided a dynamic analysis apparatus,including: a setting section which sets a plurality of target regions ina lung region of a chest dynamic image which is obtained by radiationimaging; a conversion section which calculates a representative value ofa pixel signal value in the target region set by the setting section,and converts the pixel signal value in the target region on the basis ofthe calculated representative value; an extraction section whichextracts a pulmonary blood flow signal from the chest dynamic imageafter conversion by the conversion section; and a calculation sectionwhich calculates a change amount of the pulmonary blood flow signalextracted by the extraction section, and calculates a feature amountregarding pulmonary blood flow on the basis of the calculated changeamount of the pulmonary blood flow signal, wherein the setting sectiondetermines a size of the target region on the basis of a size of a bodypart other than a lung blood vessel in the chest dynamic image, amovement amount of a body part other than the lung blood vessel orsubject information attached to the chest dynamic image, the subjectinformation being information regarding a subject of the radiationimaging, and the setting section sets the target region having thedetermined size in the lung region of the chest dynamic image.

According to another aspect of the present invention, there is provideda dynamic analysis system, including: an imaging apparatus which obtainsa chest dynamic image by performing radiation imaging of a dynamic stateat a chest of a human body; and the dynamic analysis apparatus accordingto claim 1.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, advantages and features of the presentinvention will become more fully understood from the detaileddescription given hereinafter and the appended drawings which are givenby way of illustration only, and thus are not intended as a definitionof the limits of the present invention, and wherein:

FIG. 1 is a view showing the entire configuration of a dynamic analysissystem in an embodiment of the present invention;

FIG. 2 is a flowchart showing imaging control processing executed by acontrol section of an imaging console in FIG. 1;

FIG. 3 is a flowchart showing image analysis processing executed by acontrol section of a diagnostic console in FIG. 1;

FIG. 4A is a view showing an example of a conversion table between a sexand a target region size;

FIG. 4B is a view showing an example of a conversion table between anage and a target region size;

FIG. 5 is a view for explaining a determination method of a measurementpoint when a movement amount of diaphragm is measured;

FIG. 6A is a view for explaining a determination method of arepresentative point when a movement amount of a rib is measured;

FIG. 6B is a view for explaining the determination method ofrepresentative point when the movement amount of the rib is measured;

FIG. 7 is a view showing a conversion example of signal values in a casewhere set target regions overlap each other;

FIG. 8A is a view showing an example of original signal waveform;

FIG. 8B is a view showing an example of average signal waveform;

FIG. 8C is a view showing a state in which the original signal waveformin FIG. 8A and the average signal waveform in FIG. 8B are superposed;

FIG. 8D is a view showing a blood flow signal component which isextracted by subtracting the average signal in FIG. 8B from the originalsignal component in FIG. 8A;

FIG. 9A is a view for explaining an example of feature amount indicatinga speed of pulmonary blood flow;

FIG. 9B is a view for explaining another example of feature amountindicating the speed of pulmonary blood flow;

FIG. 10A is a view showing a display example of a calculation result offeature amount regarding a lung in a case where the target regions donot overlap each other;

FIG. 10B is a view showing a display example of a calculation result offeature amount regarding a lung in a case where the target regionsoverlap each other;

FIG. 10C is a view showing a result display example of a pulmonary bloodflow direction; and

FIG. 11 is a view for explaining normalization of position of lungregion when pulmonary blood flow is compared between different dynamicimages.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Hereinafter, an embodiment of the present invention will be described indetail with reference to the drawings. However, the scope of the presentinvention is not limited to the illustrated examples.

[Configuration of Dynamic Analysis System 100]

First, the configuration will be described.

FIG. 1 shows the entire configuration of a dynamic analysis system 100in the embodiment.

As shown in FIG. 1, the dynamic analysis system 100 is configured byconnecting an imaging apparatus 1 to an imaging console 2 by acommunication cable and such like, and connecting the imaging console 2to a diagnostic console 3 via a communication network NT such as a LAN(Local Area Network). The apparatuses forming the dynamic analysissystem 100 are compliant with the DICOM (Digital Image andCommunications in Medicine) standard, and the apparatuses arecommunicated with each other according to the DICOM.

[Configuration of Imaging Apparatus 1]

The imaging apparatus 1 performs imaging of a dynamic state having acycle at a chest, such as the state change of inflation and deflation ofa lung according to respiratory motion and the heart beat, for example.The dynamic imaging means obtaining a plurality of images showing adynamic state by repeatedly emitting a pulsed radiation such as X-ray toa subject at a predetermined time interval (pulse irradiation) orcontinuously emitting the radiation (continuous irradiation) at a lowdose rate without interruption. A series of images obtained by thedynamic imaging is referred to as a dynamic image. Each of the pluralityof images forming the dynamic image is referred to as a frame image.Hereinafter, the embodiment will be described by taking, as an example,a case where the dynamic imaging is performed by pulse irradiation.

A radiation source 11 is located at a position facing a radiationdetection section 13 through a subject M, and emits radiation (X ray) tothe subject M in accordance with control of an irradiation controlapparatus 12.

The irradiation control apparatus 12 is connected to the imaging console2, and performs radiation imaging by controlling the radiation source 11on the basis of an irradiation condition which was input from theimaging console 2. The irradiation condition input from the imagingconsole 2 is a pulse rate, a pulse width, a pulse interval, the numberof imaging frames per imaging, a value of X-ray tube current, a value ofX-ray tube voltage and a type of applied filter, for example. The pulserate is the number of irradiation per second and consistent with anafter-mentioned frame rate. The pulse width is an irradiation timerequired for one irradiation. The pulse interval is a time from start ofone irradiation to start of next irradiation, and consistent with anafter-mentioned frame interval.

The radiation detection section 13 is configured by including asemiconductor image sensor such as an FPD. The FPD has a glasssubstrate, for example, and a plurality of detection elements (pixels)is arranged in matrix at a predetermined position on the substrate todetect, according to the intensity, at least radiation which was emittedfrom the radiation source 11 and has transmitted through the subject M,and convert the detected radiation into electric signals to beaccumulated. Each pixel is formed of a switching section such as a TFT(Thin Film Transistor), for example. The FPD may be an indirectconversion type which converts X ray into an electrical signal byphotoelectric conversion element via a scintillator, or may be a directconversion type which directly converts X ray into an electrical signal.

The radiation detection section 13 is provided to face the radiationsource 11 via the subject M.

The reading control apparatus 14 is connected to the imaging console 2.The reading control apparatus 14 controls the switching sections ofrespective pixels in the radiation detection section 13 on the basis ofan image reading condition input from the imaging console 2, switchesthe reading of electric signals accumulated in the pixels, and reads outthe electric signals accumulated in the radiation detection section 13to obtain image data. The image data is a frame image. The readingcontrol apparatus 14 outputs the obtained frame image to the imagingconsole 2. The image reading condition includes a frame rate, frameinterval, a pixel size, an image size (matrix size) and such like. Theframe rate is the number of frame images obtained per second andconsistent with the pulse rate. The frame interval is a time from startof obtaining one frame image to start of obtaining the next frame image,and consistent with the pulse interval.

Here, the irradiation control apparatus 12 and the reading controlapparatus 14 are connected to each other, and transmit synchronizingsignals to each other to synchronize the irradiation operation with theimage reading operation.

[Configuration of Imaging Console 2]

The imaging console 2 outputs the irradiation condition and the imagereading condition to the imaging apparatus 1, controls the radiationimaging and reading operation of radiation images by the imagingapparatus 1, and displays the dynamic image obtained by the imagingapparatus 1 for an operator who performs the imaging such as an imagingoperator to confirm positioning and whether the image is appropriate fordiagnosis.

As shown in FIG. 1, the imaging console 2 is configured by including acontrol section 21, a storage section 22, an operation section 23, adisplay section 24 and a communication section 25, which are connectedto each other via a bus 26.

The control section 21 is configured by including a CPU (CentralProcessing Unit), a RAM (Random Access Memory) and such like. Accordingto the operation of operation section 23, the CPU of the control section21 reads out system programs and various processing programs stored inthe storage section 22 to load the programs into the RAM, executesvarious types of processing including after-mentioned imaging controlprocessing in accordance with the loaded program, and integrallycontrols the operations of sections in the imaging console 2 and theirradiation operation and reading operation of the imaging apparatus 1.

The storage section 22 is configured by including a non-volatilesemiconductor memory and a hard disk. The storage section 22 storesvarious programs executed by the control section 21, parametersnecessary for executing processing by the programs, and data ofprocessing results. For example, the storage section 22 stores a programfor executing the imaging control processing shown in FIG. 2. Thestorage section 22 stores the irradiation condition and the imagereading condition corresponding to the imaging site (here, chest). Thevarious programs are stored in a form of readable program code, and thecontrol section 21 executes the operation according to the program codeas needed.

The operation section 23 is configured by including a keyboard includingcursor keys, numeric keys and various function keys and a pointingdevice such as a mouse. The operation section 23 outputs an instructionsignal input by a key operation to the keyboard or a mouse operation tothe control section 21. The operation section 23 may include a touchpanel on the display screen of display section 24. In this case, theoperation section 23 outputs the input instruction signal to the controlsection 21 via the touch panel.

The display section 24 is configured by a monitor such as an LCD (LiquidCrystal Display) and a CRT (Cathode Ray Tube), and displays instructionsinput from the operation section 23, data and such like in accordancewith an instruction of a display signal input from the control section21.

The communication section 25 includes a LAN adapter, a modem, a TA(Terminal Adapter) and such like, and controls the data transmission andreception with the apparatuses connected to the communication networkNT.

[Configuration of Diagnostic Console 3]

The diagnostic console 3 is a dynamic analysis apparatus for obtainingthe dynamic image from the imaging console 2 and displaying the obtaineddynamic image and analysis result of the dynamic image to supportdiagnosis by a doctor. In the embodiment, the diagnostic console 3analyses a dynamic image of chest for a pulmonary blood flow function,and displays the analysis result.

As shown in FIG. 1, the diagnostic console 3 is configured by includinga control section 31, a storage section 32, an operation section 33, adisplay section 34 and a communication section 35, which are connectedto each other via a bus 36.

The control section 31 is configured by including a CPU, a RAM and suchlike. According to the operation of the operation section 33, the CPU ofthe control section 31 reads out system programs stored in the storagesection 32 and various processing programs to load them into the RAM,executes the various types of processing including after-mentioned imageanalysis processing in accordance with the loaded program, andintegrally controls operations of the sections in the diagnostic console3. The control section 31 functions as a setting section, a conversionsection, an extraction section, a calculation section and a comparisonsection.

The storage section 32 is configured by including a non-volatilesemiconductor memory, a hard disk and such like. The storage section 32stores various programs including a program for executing the imageanalysis processing by the control section 31, parameters necessary forexecuting processing by the programs and data of processing results. Thevarious programs are stored in a form of readable program code, and thecontrol section 31 executes the operation according to the program codeas needed.

The operation section 33 is configured by including a keyboard includingcursor keys, numeric keys and various function keys and a pointingdevice such as a mouse, and outputs an instruction signal input by a keyoperation to the keyboard and a mouse operation to the control section31. The operation section 33 may include a touch panel on the displayscreen of the display section 34. In this case, the operation section 33outputs an instruction signal, which was input via the touch panel, tothe control section 31.

The display section 34 is configured by including a monitor such as anLCD and a CRT, and performs various displays in accordance with theinstruction of display signal input from the control section 31.

The communication section 35 includes a LAN adapter, a modem, a TA andsuch like, and controls data transmission and reception with theapparatuses connected to the communication network NT.

[Operation of Dynamic Analysis System 100]

Next, the operation of the dynamic analysis system 100 will bedescribed.

(Operations of Imaging Apparatus 1 and Imaging Console 2)

First, imaging operation by the imaging apparatus/land the imagingconsole 2 will be described.

FIG. 2 shows imaging control processing executed by the control section21 in the imaging console 2. The imaging control processing is executedin cooperation between the control section 21 and the program stored inthe storage section 22.

First, the operator operates the operation section 23 in the imagingconsole 2, and inputs patient information (patient name, height, weight,age, sex and such like) of the imaging target (subject M) and imagingsite (here, chest) (step S1).

Next, the irradiation condition is read out from the storage section 22and set in the irradiation control apparatus 12, and the image readingcondition is read out from the storage section 22 and set in the readingcontrol apparatus 14 (step S2).

An instruction of irradiation by the operation of operation section 23is waited (step S3). The operator locates the subject M between theradiation source 11 and the radiation detection section 13, and performspositioning. Since the imaging is performed while the subject M isbreathing in the embodiment, the operator instructs the subject ofimaging (subject M) to be at ease to lead the subject M into quietbreathing. When the preparation for imaging is completed, the operatoroperates the operation section 23 to input an irradiation instruction.

When the irradiation instruction is input from the operation section 23(step S3:YES), the imaging start instruction is output to theirradiation control apparatus 12 and the reading control apparatus 14,and the dynamic imaging is started (step S4). That is, radiation isemitted by the radiation source 11 at the pulse interval set in theirradiation control apparatus 12, and frame images are obtained by theradiation detection section 13.

When the imaging is finished for a predetermined number of frames, thecontrol section 21 outputs an instruction to end the imaging to theirradiation control apparatus 12 and the reading control apparatus 14,and the imaging operation is stopped. The imaging is performed to obtainthe number of frame images which can image at least one respiratorycycle.

The frame images obtained by the imaging are input to the imagingconsole 2 in order, stored in the storage section 22 so as to beassociated with respective numbers (frame numbers) indicating theimaging order (step S5), and displayed on the display section 24 (stepS6). The operator confirms positioning and such like by the displayeddynamic image, and determines whether an image appropriate for diagnosiswas acquired by the imaging (imaging was successful) or imaging needs tobe performed again (imaging failed). The operator operates the operationsection 23 and inputs the determination result.

If the determination result indicating that the imaging was successfulis input by a predetermined operation of the operation section 23 (stepS7:YES), each of a series of frame images obtained by the dynamicimaging is accompanied with information such as identification ID foridentifying the dynamic image, patient information, imaging site,irradiation condition, image reading condition and number (frame number)indicating the imaging order (for example, the information is writteninto a header region of image data in the DICOM format), and transmittedto the diagnostic console 3 via the communication section 25 (step S8).Then, the processing ends. On the other hand, if the determinationresult indicating that the image failed is input by a predeterminedoperation of operation section 23 (step S7:NO), the series of frameimages stored in the storage section 22 is deleted (step S9), and theprocessing ends. In this case, the imaging needs to be performed again.

(Operation of Diagnostic Console 3)

Next, the operation of diagnostic console 3 will be described.

In the diagnostic console 3, when a series of frame images forming adynamic image is received from the imaging console 2 via thecommunication section 35, the image analysis processing shown in FIG. 3is executed in cooperation between the control section 31 and theprogram stored in the storage section 32.

Hereinafter, the flow of image analysis processing will be describedwith reference to FIG. 3.

First, a lung region which is the analysis target region is extractedfrom each of the frame images forming the dynamic image (step S11).

Any method may be used as the method for extracting the lung region. Forexample, a threshold value is obtained by a discriminant analysis fromhistogram of signal value (density value) for each pixel of the frameimage, and the region having higher signals than the threshold value isprimarily extracted as a lung region candidate. Then, edge detection isperformed around the border of the lung region candidate which wasprimarily extracted, and the points having largest edges in small blocksaround the border are extracted along the border to extract the borderof lung region.

A target region which is a unit of noise removal is set in the lungregion of each frame image forming the dynamic image (step S12).

In step S12, the size of target region is determined first. The targetregion size is determined on the basis of, for example, one of i) sizeof lung, ii) patient information (sex, age, height and such like ofpatient), iii) movement amount of diaphragm and iv) movement amount ofrib. Hereinafter, the method for determining the target region sizebased on each of the above items will be described.

i) Determination of Target Region Size Based on the Size of Lung

As a condition for determining the target region size by thisdetermination method, mean areas of lung (mean area of right lung: Mean(Sright) and mean area of left lung: Mean (Sleft)) among a plurality ofdifferent human bodies and a standard target region size (Broi_x,Broi_y) are stored in the program in advance. The standard target regionsize is the most appropriate target region size for the lung having anaverage area, and obtained experimentally and empirically.

First, the mean areas (mean area of right lung: Sright, mean area ofleft lung: Sleft) are calculated for the lungs in the series of frameimages forming the dynamic image. For example, an area is calculated bycounting the number of pixels in a region and multiplying the countednumber by the pixel size for each of the right lung region and left lungregion in each of the frame images, and the mean value of the calculatedareas of the respective frame images is calculated.

Next, on the basis of the calculated mean area, (Roix, Roiy) iscalculated by the following (Expression 1) and (Expression 2), and thecalculated size is determined as the target region size (width×length).Though the (Expression 1) and (Expression 2) are expressions for theright lung, the target region size of left lung can also be calculatedsimilarly.Roix=Broi_x×(Sright/Mean (Sright)  (Expression 1)Roiy=Broi_y×(Sright/Mean (Sright  (Expression 2)

By determining the target region size on the basis of the area of lung,the target region size can be set to be the most appropriate sizeaccording to the lung size. As a result, for example, it is possible toprevent a large target region appropriate for an adult from being set toa small lung of a child.

ii) Determination of Target Region Size Based on Patient Information(Sex, Age and Height of Patient)

Since males perform abdominal breathing dominantly, males move theirlungs more largely than females. In addition, the lung moves worse withaging. Thus, a conversion table (see FIGS. 4A and 4B) associating thetarget region size with each of the sex and age is stored in the storagesection 32, the conversion table is referred to, and the target regionsize is determined on the basis of sex and age of the patientinformation (subject of the imaging) included in the accompanyinginformation attached to the dynamic image. Though FIGS. 4A and 4Brespectively show the conversion tables of sex and age, an age-basedconversion table for a male and an age-based conversion table for afemale may be stored in the storage section 32. The base fordetermination is not limited to sex and age, and a conversion tableassociating the target region size with other physical information suchas height and weight may be stored in the storage section 32.

iii) Determination of Target Region Size Based on Movement Amount ofDiaphragm

The movement amount of diaphragm can be obtained by a method describedin a known document 1, for example (known document 1: Development oflow-cost and low-dose portable functional X-ray imaging “Visualstethoscope”, Rie TANAKA, Shigeru SANADA, Medical Imaging andInformation Sciences

Vol. 31 (2014) No. 2). In the first frame, the four measurement pointsshown in FIG. 5 (an apex in the lung apex and a point in the diaphragmdome for each of left and right lungs) are determined by edge detection.In the second frame and the following frames, the measurement points aretraced by template matching. The change amount of the apex-diaphragmdistance (distance between the lung apex and the diaphragm) is thediaphragm movement amount. For example, the calculated maximum diaphragmmovement amount (difference between the largest value and the smallestvalue of the apex-diaphragm distances) is determined as a target regionsize (length of a side of the target region).

iv) Determination of Target Region Size Based on Movement Amount of Rib

First, the upper edge and lower edge of a rib are extracted from thelung region in each of the frame images. The upper and lower edges of arib can be extracted by a known image processing technique such as a ribextraction method using the model function and Sobel operator describedin Japanese Patent Application Laid Open Publication No. H5-176919, forexample. Then, the movement amount of the rib is calculated. Forexample, as shown in FIG. 6A, in a reference image (such as an image ofresting expiratory level), an intermediate curve L3 between theextracted upper edge L1 and lower edge L2 of the rib is obtained, and arepresentative point PBase is set at the intersection of theintermediate curve L3 and a line L4 which is connecting the midpoint P1in the x direction of the upper edge L1 and the midpoint P2 in the xdirection of lower edge L3 of the rib. Similarly, representative points(Pt, Pt+1, . . . ) of the same rib are set in respective other frameimages (see FIG. 6B). The distance between the representative point inthe reference image and a representative point in each of the otherframe images is obtained as a movement amount of the rib, and thelargest value among the obtained distances is determined as a targetregion size (length of a side of target region), for example. The targetregion size may be determined by considering the width of the rib inaddition to the above process.

That is, the target region size is determined according to the size ormovement amount of lung region, movement amount of a body part which isa noise for analysis, or difference between individuals such as sex andage of the subject.

When the target region size is determined, an image among a series offrame images forming the dynamic image is set as a reference image, anda target region which has the determined size and is formed of aplurality of pixels is set in the lung region of the reference image. Apreferable reference image is a frame image of a resting expiratorylevel at which the lung region has the minimum area. By the preferablereference image, when small regions in the reference image areassociated with those in each of the other frame images, the smallregions are not associated with regions outside the lung region in eachof the other frame images.

For example, the lung region in the reference image is divided into aplurality of small regions (rectangular regions) having the determinedsize, and each of the small regions is set as a target region.Alternatively, the reference image may be displayed on the displaysection 34 so that the user specifies a point on the displayed referenceimage by operating the operation section 33, and a rectangular regionwhich has the determined size and includes the specified point on theupper left side is set to be a target region. In such cases, the targetregion is set in a tiled manner without overlapping each other. Thetarget regions which are not overlapping in such way are referred to asnon-overlapping target regions. On the other hand, the target regionhaving the determined size may be set by being shifted by one pixel onthe lung region. In this case, neighboring target regions areoverlapping each other as shown in FIG. 7. The target regionsoverlapping each other in this way are referred to as overlapping targetregions.

Next, in each of the other frame images, the target regions are set atthe same pixel positions as the target regions which were set in thereference image (regions of the signal values output from the samedetection elements of radiation detection section 13 used for theimaging). In the above cases of (i) and (ii), it is preferable that thetarget regions are set in the other frame images after correcting thepositioning gaps of lung region between the frame images by performingknown local matching processing and warping processing (see JapanesePatent Application Laid Open Publication No. 2012-5729).

The signal value (pixel signal value) of each pixel in each of the frameimages is replaced with a representative value calculated from the pixelsignal values in the target region (step S13). This process suppressesthe signal components of other body parts such as ribs which are noises.

In step S13, for example, if the set target regions are non-overlappingtarget regions, the representative value of pixel signal values in thetarget region is calculated, and the signal value of each pixel in thetarget region is converted into the calculated representative value. Ifthe set target regions are overlapping target regions, as shown in FIG.7, the representative value of pixel signal values in the target regionis calculated, and the signal value of the pixel located at the centerof the target region is converted into the calculated representativevalue.

The representative value may be any one of median value, mean value,maximum value, minimum value and such like. However, the median value ispreferable since the signal components of other body parts such as ribswhich are noises can be suppressed more effectively.

The blood flow signal component (that is, pulmonary blood flow signalcomponent) is extracted from the lung region of the converted dynamicimage, and a plurality of frame images formed of the blood flow signalcomponents is generated (step S14).

As the method for extracting the blood flow signal component, there arei) method using a frequency filter, ii) method using an averagewaveform, iii) method using machine learning and such like. Hereinafter,each of the extraction methods will be described.

i) Method Using a Frequency Filter

In a case where non-overlapping target regions are set, correspondingtarget regions in a series of respective frame images are associatedwith each other, the temporal change of signal value (representativevalue) is calculated for the target region, and the calculated temporalchange is filtered by a high pass filter (for example, cutoff frequencyis 0.7 Hz) in the time direction. This process removes the influence ofventilation by removing only the blood flow signal component from thetemporal change of signal value for each target region.

In a case where overlapping target regions are set, the temporal changeof signal value (representative value) is calculated for each pixelbetween the frames, and the calculated temporal change is filtered by ahigh pass filter (for example, cutoff frequency is 0.7 Hz) in the timedirection. This process removes the influence of ventilation by removingonly the blood flow signal component from the temporal change of signalvalue for each pixel.

A bandpass filter may be used instead of the high pass filter.

ii) Method Using an Average Waveform

In a case where non-overlapping target regions are set, correspondingtarget regions in a series of respective frame images are associatedwith each other, and a waveform showing a temporal change of signalvalue is drawn for the target region. The signal waveform is referred toas an original signal waveform. FIG. 8A shows an example of the originalsignal waveform. As shown in FIG. 8A, the original signal waveform has awaveform in which a high frequency by pulmonary blood flow is superposedon a low frequency by breathing. The original signal waveform is dividedby the cycle of the low frequency, and the waveforms for a plurality ofcycles are averaged to generate an average signal waveform. FIG. 8Bshows an example of the average signal waveform. The average signalwaveform has a waveform which is nearly a sinusoidal waveform of lowfrequency as shown in FIG. 8B since the high frequency component isaveraged. As shown in FIG. 8C, the original signal waveform and theaverage signal waveform are superposed on each other, the average signalis subtracted from the original signal (difference is obtained), andthereby, the high frequency component is obtained as shown in FIG. 8D.That is, the blood flow signal component can be extracted.

In a case where overlapping target regions are set, a waveform showing atemporal change of signal value (representative value) is drawn for eachcorresponding pixels in the respective frame images, and similarly, anaverage signal waveform is generated by averaging the waveforms forrespective cycles of the original signal waveform. The average signal issubtracted from the original signal to extract the blood flow signalcomponent.

iii) Method Using Machine Learning

A plurality of dynamic images imaging the dynamic state at a chest of ahuman body is prepared, and each of the images is filtered by a low passfilter (for example, cutoff frequency is 0.5 Hz) in the time directionto extract the signal of ventilation component. The extracted signal ofventilation component is subjected to signal reconstruction (see knowndocument 2) by pre-training in deep learning such as RPB (RestrictedBoltzmann Machine) and Auto Encoder, and a signal having a generalfeature of the ventilation component is generated (known document 2: AStudy on Usage of Reconstruction Error of Deep Learning for AnomalyDetection on Time-Series Data, Koki Kawasaki, Tomohiro Yoshikawa,Takeshi Furuhashi, The 29th annual conference of the Japanese societyfor Artificial Intelligence, 2015). In a case where non-overlappingtarget regions are set, corresponding target regions in a series ofrespective frame images are associated with each other, and a waveform(original signal waveform) showing a temporal change of a signal valuefor the target region is generated, a signal having a general feature ofventilation component is subtracted from the original signal waveform(difference is obtained), and thereby the blood flow signal component isextracted. In a case where overlapping target regions are set, anoriginal signal waveform showing the temporal change of signal value isgenerated for each corresponding pixels in the frame images, a signalhaving a general feature of ventilation component is subtracted from theoriginal signal waveform (difference is obtained), and thereby a bloodflow signal component is extracted.

Next, by performing inter-frame difference processing of the extractedblood flow signal component, the change amount of blood flow signalcomponent is calculated (step S15).

In a case where non-overlapping target regions are set, the differencevalue of signal value between adjacent frame images is calculated foreach target region. In a case where overlapping target regions are set,the difference value of signal value between adjacent frame images iscalculated for each pixel, that is, for the corresponding pixels in therespective frames. Here, the inter-frame difference value is obtained bysubtracting a value of a previous frame image from a value of the frameimage temporally following the previous frame image.

Next, a feature amount of pulmonary blood flow is calculated on thebasis of the change amount of the blood flow signal component (stepS16).

Here, the calculated feature amount is at least one of the featureamounts indicating i) speed of pulmonary blood flow, ii) amount ofpulmonary blood flow and iii) direction of pulmonary blood flow. In acase where non-overlapping target regions are set, the feature amount iscalculated for each target region. In a case where overlapping targetregions are set, the feature amount is calculated for each pixel.

i) Speed of Pulmonary Blood Flow

For example, a cycle T[s] is calculated as the feature amount indicatingthe speed of pulmonary blood flow, the cycle T[s] being a cycle oftemporal change regarding the change amount (that is, inter-framedifference value) of blood flow signal component which was calculated instep S15 (see FIG. 9A). Alternatively, a time T1 is calculated as thefeature amount indicating the speed of pulmonary blood flow, the time T1being a time required from a maximum value to the next minimum value inthe temporal change of the change amount of the blood flow signalcomponent calculated in step S15 (see FIG. 9B). Alternatively, adifferential value is calculated as the feature amount indicating thespeed of pulmonary blood flow, the differential value being adifferential value of the temporal change of the change amount of theblood flow signal component calculated in step S15. The calculationresult is stored in the RAM of control section 31.

ii) Amount of Pulmonary Blood Flow

For example, as the feature amount indicating the amount of pulmonaryblood flow, a maximum value or a minimum value in the temporal change ofchange amount of blood flow signal component calculated in step S15 iscalculated. Alternatively, the rate between maximum value and minimumvalue (maximum value/minimum value) is calculated as the feature amountindicating the amount of pulmonary blood flow. The rate between maximumvalue and minimum value can show the balance of amount rate betweeninflow and outflow of blood. The calculation result is stored in the RAMof control section 31.

iii) Direction of Pulmonary Blood Flow

The direction of pulmonary blood flow is obtained if the blood flow inthe lung region decreases between frame images and the blood flowincreases, in the next frame image, near the position where the bloodflow decreased. Here, when the blood flow decreases, the signal value(density value) of dynamic image is changed from low (white) to high(black), and thus, the inter-frame difference value is positive. Whenthe blood flow increases, the signal value (density value) of dynamicimage is changed from high (black) to low (white), and thus, theinter-frame difference value is negative. Thus, for each of the targetregions (pixels), whether the inter-frame difference value for the t-thframe image is positive or negative is determined. If the inter-framedifference value for a target region (pixel) is positive, the directionof pulmonary blood flow is specified to be the region (pixel) which hasthe smallest inter-frame difference value among the neighboring regions(for example, eight regions (pixels) around the target region (pixel))in the “t+1”-th frame image. The information regarding the direction ofpulmonary blood flow is stored in the RAM of control section 31.

As described above, in step S16, it is possible to calculate a pluralityof indexes indicating pulmonary blood flow functions such as speed,amount and direction of pulmonary blood flow in addition to the changeamount of pulmonary blood flow in each of the localized regions of thelung.

Next, the calculation results of the feature amounts of pulmonary bloodflow are displayed on the display section 34 (step S17).

For example, in a case where the calculated feature amounts indicate i)speed of pulmonary blood flow or ii) amount of pulmonary blood flow, animage having the target regions (or pixels) in respective colorscorresponding to the calculated feature amounts is displayed on thedisplay section 34. FIG. 10A shows an example of result display in acase of non-overlapping target regions. FIG. 10B shows an example ofresult display in a case of overlapping target regions.

In a case where the calculated feature amounts indicate the iii)direction of pulmonary blood flow, for example, an image with arrowsindicating the directions of blood flow in the lung is displayed on thedisplay section 34. FIG. 10C shows an example of result display of thedirections of pulmonary blood flow.

By comparing the pulmonary blood flow function of the central regionclose to the heart with the pulmonary blood flow function of peripheralregions, it is possible to recognize diseases such as bad blood flow ata peripheral region. That is, by comparing the feature amounts of tworegions on the dynamic image, a disease can be found in some cases.Thus, for example, the control section 31 may calculate one of thefollowing (Expression 3) to (Expression 6) to compare the featureamounts of two regions on the image shown in FIG. 10A or FIG. 10B (orreference image), the two regions being specified by a user operatingthe operation section 33.rate: A/B  (Expression 3)difference: |A−B|  (Expression 4)cosine similarity: cos θ=A·B/|A∥B|  (Expression 5)Pearson Correlation:

$\begin{matrix}{r = \frac{\sum\limits_{i = 1}^{n}{\left( {A_{i} - \overset{\_}{A}} \right)\left( {B_{i} - \overset{\_}{B}} \right)}}{\sqrt{\sum\limits_{i = 1}^{n}{\left( {A_{i} - \overset{\_}{A}} \right)^{2}{\sum\limits_{i = 1}^{n}\left( {B_{i} - \overset{\_}{B}} \right)^{2}}}}}} & \left( {{Expression}\mspace{14mu} 6} \right)\end{matrix}$

n: number of compared feature amounts

Here, A is a feature amount of one of the two regions to be compared,and B is a feature amount of the other region to be compared.

In a case where a single feature amount is used for the comparison, therate or difference is calculated. In a case where a plurality of featureamounts is used for the comparison, the cosine similarity or Pearsoncorrelation is calculated. The calculated value is displayed on thedisplay section 34.

Alternatively, waveforms showing temporal changes of pixel signal valuesat two regions specified by the operation section 33 may be displayed onthe display section 34 so as to be compared with each other.

In a case where the pulmonary blood flow function is to be comparedbetween different dynamic images such as lung images of a same subjectcaptured at different times and lung images of different subjects(different patients), the lung position is normalized in order toassociate the positions of target regions (pixels) of the lung in thetwo images with each other. For example, when the comparison isperformed between lungs of different dynamic images, a horizontalprofile in the x direction is obtained for each of the two dynamicimages as shown in FIG. 11, and the position of minimum value of theobtained horizontal profile is determined as the position of midline.Then, the position coordinates of lung region are determined so that theintersection between the midline and the upper end of rectangular regioncircumscribing the lung region in the frame image is (0, 0), the upperright of rectangular region is (1, 0), the upper left of rectangularregion is (−1, 0), the lower right of rectangular region is (1, 1), andthe lower left of rectangular region is (−1, 1). The midline may belocated at the center of the rectangular region circumscribing the lungregion.

As described above, according to the diagnostic console 3, the controlsection 31 determines a size of target region on the basis of a size ofa body part other than the lung blood vessel in the chest dynamic image,a movement amount of a body part other than the lung blood vessel, orsubject information attached to the chest dynamic image, the subjectinformation being information regarding the subject of imaging. Thecontrol section 31 sets a plurality of target regions having thedetermined size in the lung region of chest dynamic image. For example,the control section 31 sets the target region size on the basis of thelung size in the chest dynamic image, movement amount of diaphragm,movement amount of rib, or the subject information attached to the chestdynamic image.

Accordingly, it is possible to set an appropriate size of target regionin consideration of the influence such as the movement of a body partand the difference between individuals for suppressing noise whenanalyzing the pulmonary blood flow function in the chest dynamic image.

The control section 31 calculates a representative value of pixel signalvalues in a set target region, converts the pixel signal values in thetarget region on the basis of the calculated representative value,extracts the pulmonary blood flow signal from the converted chestdynamic image, and calculates the change amount of the extractedpulmonary blood flow signal. The control section 31 calculates thefeature amount regarding pulmonary blood flow on the basis of thecalculated change amount of pulmonary blood flow signal.

For example, the control section 31 calculates, as the change amount ofpulmonary blood flow signal, the difference value of pixel signal valuebetween frame images generated by extracting the pulmonary blood flowsignal from the chest dynamic image, and calculates, as the featureamount regarding the speed of pulmonary blood flow, the cycle oftemporal change of the difference value between the frame images, thetime required from a maximum point to a minimum point, or a differentialvalue. The control section 31 calculates, as the change amount ofpulmonary blood flow signal, the difference value of pixel signal valuebetween frame images generated by extracting the pulmonary blood flowsignal from the chest dynamic image, and calculates, as the featureamount regarding the amount of pulmonary blood flow, the maximum value,minimum value or the rate between maximum value and minimum value in thetemporal change of difference value between frame images. Further, forexample, the control section 31 calculates, as the change amount ofpulmonary blood flow signal, the difference value of pixel signal valuebetween frame images generated by extracting the pulmonary blood flowsignal from the chest dynamic image, specifies the change direction ofpulmonary blood flow signal on the basis of the calculated differencevalue between frame images, and calculates the specified direction asthe feature amount regarding the direction of pulmonary blood flow.

Accordingly, it is possible to provide the information regarding thespeed, amount and direction of pulmonary blood flow as diagnosis supportinformation.

The description of the embodiment is an example of a preferred dynamicanalysis system according to the present invention, and the presentinvention is not limited to this embodiment.

For example, the embodiment has been described for an example of using ahard disk or semiconductor non-volatile memory and such like as acomputer readable medium of program according to the present invention.However, the present invention is not limited to this example. Aportable recording medium such as a CD-ROM can be applied as a computerreadable medium. A carrier wave is also applied as the medium forproviding program data according to the present invention via acommunication line.

As for the other detailed configurations and detailed operations ofapparatuses forming the dynamic analysis system 100, modifications canbe appropriately made within the scope of the present invention.

The disclosed embodiment is for illustration and not for limitation ofthe present invention in all respects. The scope of the presentinvention is indicated by the scope of claims, not by the abovedescription. The scope of the present invention includes the scope ofinventions, which is described in the scope of claims, the scopeequivalent thereof and all the modifications within the scope of claims.

What is claimed is:
 1. A dynamic analysis apparatus, comprising: asetting section which sets a target region in a lung region of a chestdynamic image which is obtained by radiation imaging; a conversionsection which calculates a representative value of a pixel signal valuein the target region set by the setting section, and converts the pixelsignal value in the target region on the basis of the calculatedrepresentative value; an extraction section which extracts a pulmonaryblood flow signal from the chest dynamic image after conversion by theconversion section; and a calculation section which calculates a changeamount of the pulmonary blood flow signal extracted by the extractionsection, and calculates a feature amount regarding pulmonary blood flowon the basis of the calculated change amount of the pulmonary blood flowsignal, wherein the setting section determines a size of the targetregion on the basis of a size of a body part other than a lung bloodvessel in the chest dynamic image, a movement amount of a body partother than the lung blood vessel or subject information attached to thechest dynamic image, the subject information being information regardinga subject of the radiation imaging, and the setting section sets thetarget region having the determined size in the lung region of the chestdynamic image.
 2. The dynamic analysis apparatus according to claim 1,wherein the setting section sets a plurality of target regions in thelung region of the chest dynamic image.
 3. The dynamic analysisapparatus according to claim 1, wherein the setting section determinesthe size of the target region on the basis of a size of a lung in thechest dynamic image, a movement amount of a diaphragm, a movement amountof a rib or the subject information attached to the chest dynamic image.4. The dynamic analysis apparatus according to claim 1, wherein theextraction section extracts the pulmonary blood flow signal byperforming frequency filter processing in a time direction to a temporalchange of the pixel signal value in the chest dynamic image afterconversion by the conversion section.
 5. The dynamic analysis apparatusaccording to claim 1, wherein the extraction section extracts thepulmonary blood flow signal by subtracting an average signal waveformfrom a temporal change waveform showing a temporal change of the pixelsignal value in the chest dynamic image after conversion by theconversion section, the average signal waveform being obtained byaveraging a plurality of waveforms which are obtained by dividing thetemporal change waveform by a cycle of a low frequency componentincluded in the temporal change waveform.
 6. The dynamic analysisapparatus according to claim 1, wherein the extraction section extractsthe pulmonary blood flow signal by subtracting a signal waveform of aventilation component generated by using machine learning from atemporal change waveform showing a temporal change of the pixel signalvalue in the chest dynamic image after conversion by the conversionsection.
 7. The dynamic analysis apparatus according to claim 1, whereinthe calculation section calculates, as the change amount of thepulmonary blood flow signal, a difference value of the pixel signalvalue between frame images generated by extracting the pulmonary bloodflow signal by the extraction section, and the calculation sectioncalculates, as a feature amount regarding a speed of the pulmonary bloodflow, a cycle, a time required from a maximum point to a minimum pointor a differential value of a temporal change of the calculateddifference value between the frame images.
 8. The dynamic analysisapparatus according to claim 1, wherein the calculation sectioncalculates, as the change amount of the pulmonary blood flow signal, adifference value of the pixel signal value between frame imagesgenerated by extracting the pulmonary blood flow signal by theextraction section, and the calculation section calculates, as a featureamount regarding an amount of the pulmonary blood flow, a maximum value,a minimum value or a rate between the maximum value and the minimumvalue of a temporal change of the calculated difference value betweenthe frame images.
 9. The dynamic analysis apparatus according to claim1, wherein the calculation section calculates, as the change amount ofthe pulmonary blood flow signal, a difference value of the pixel signalvalue between frame images generated by extracting the pulmonary bloodflow signal by the extraction section, identifies a change direction ofthe pulmonary blood flow signal on the basis of the calculateddifference value between the frame images, and calculates the identifieddirection as a feature amount regarding a direction of the pulmonaryblood flow.
 10. The dynamic analysis apparatus according to claim 1,further comprising a comparison section which compares the featureamount regarding the pulmonary blood flow calculated by the calculationsection or a waveform showing a temporal change of the pulmonary bloodflow signal between two or more regions in the lung region.
 11. Adynamic analysis system, comprising: an imaging apparatus which obtainsa chest dynamic image by performing radiation imaging of a dynamic stateat a chest of a human body; and the dynamic analysis apparatus accordingto claim
 1. 12. The dynamic analysis system according to claim 11,further comprising a display section which displays an analysis resultby the dynamic analysis apparatus.